Identifying conserved and divergent transcriptional modules by cross-species matrix decomposition on microarray data

Cross-species comparison of gene expression profiles allows deciphering fundamental and species-specific transcriptional programs of cells and offers insight into organization and evolution of the genome and genetic network. Here, we propose an algorithm for comparing microarray data from different species to unravel transcriptional modules that are conserved or divergent through evolution. The proposed algorithm is based on cross-species matrix decomposition that includes a nonlinear independent component analysis followed a generalized probabilistic sparse matrix factorization on microarray data from different species. The proposed algorithm captures transcriptional modularity that might result from highly nonlinear interactions among genes, and partitions genes into mutually non-exclusive transcriptional modules. The conserved transcriptional modules are identified by the latent variables that are associated with predominant biological prototypes shared across species. We illustrated the application of the proposed algorithm by an analysis of human and mouse embryonic stem cell (ESC) data. The analysis uncovered conserved and divergent transcriptional modules in the ESC transcriptomes, shedding light on the understanding of fundamental and species-specific regulatory mechanisms controlling ESC development.